Cargando…
Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia
BACKGROUND: To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19). METHODS: In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID...
Autores principales: | Chen, Hui Juan, Mao, Li, Chen, Yang, Yuan, Li, Wang, Fei, Li, Xiuli, Cai, Qinlei, Qiu, Jie, Chen, Feng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424152/ https://www.ncbi.nlm.nih.gov/pubmed/34496794 http://dx.doi.org/10.1186/s12879-021-06614-6 |
Ejemplares similares
-
CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias
por: Cardobi, Nicolò, et al.
Publicado: (2021) -
The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia
por: Tan, Hui-Bin, et al.
Publicado: (2020) -
Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics
por: Moradi Khaniabadi, Pegah, et al.
Publicado: (2022) -
Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features
por: Yang, Zhiqi, et al.
Publicado: (2022) -
The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT
por: Xiong, Fei, et al.
Publicado: (2021)